{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>City</th>\n",
       "      <th>Monthly_Income</th>\n",
       "      <th>DOB</th>\n",
       "      <th>Lead_Creation_Date</th>\n",
       "      <th>Loan_Amount_Applied</th>\n",
       "      <th>Loan_Tenure_Applied</th>\n",
       "      <th>Existing_EMI</th>\n",
       "      <th>Employer_Name</th>\n",
       "      <th>...</th>\n",
       "      <th>Interest_Rate</th>\n",
       "      <th>Processing_Fee</th>\n",
       "      <th>EMI_Loan_Submitted</th>\n",
       "      <th>Filled_Form</th>\n",
       "      <th>Device_Type</th>\n",
       "      <th>Var2</th>\n",
       "      <th>Source</th>\n",
       "      <th>Var4</th>\n",
       "      <th>LoggedIn</th>\n",
       "      <th>Disbursed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID000002C20</td>\n",
       "      <td>Female</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>20000</td>\n",
       "      <td>23-May-78</td>\n",
       "      <td>15-May-15</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>CYBOSOL</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>Web-browser</td>\n",
       "      <td>G</td>\n",
       "      <td>S122</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID000004E40</td>\n",
       "      <td>Male</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>35000</td>\n",
       "      <td>7-Oct-85</td>\n",
       "      <td>4-May-15</td>\n",
       "      <td>200000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TATA CONSULTANCY SERVICES LTD (TCS)</td>\n",
       "      <td>...</td>\n",
       "      <td>13.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6762.9</td>\n",
       "      <td>N</td>\n",
       "      <td>Web-browser</td>\n",
       "      <td>G</td>\n",
       "      <td>S122</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID000007H20</td>\n",
       "      <td>Male</td>\n",
       "      <td>Panchkula</td>\n",
       "      <td>22500</td>\n",
       "      <td>10-Oct-81</td>\n",
       "      <td>19-May-15</td>\n",
       "      <td>600000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ALCHEMIST HOSPITALS LTD</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>Web-browser</td>\n",
       "      <td>B</td>\n",
       "      <td>S143</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID000008I30</td>\n",
       "      <td>Male</td>\n",
       "      <td>Saharsa</td>\n",
       "      <td>35000</td>\n",
       "      <td>30-Nov-87</td>\n",
       "      <td>9-May-15</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>BIHAR GOVERNMENT</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>Web-browser</td>\n",
       "      <td>B</td>\n",
       "      <td>S143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID000009J40</td>\n",
       "      <td>Male</td>\n",
       "      <td>Bengaluru</td>\n",
       "      <td>100000</td>\n",
       "      <td>17-Feb-84</td>\n",
       "      <td>20-May-15</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>GLOBAL EDGE SOFTWARE</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>Web-browser</td>\n",
       "      <td>B</td>\n",
       "      <td>S134</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID  Gender       City  Monthly_Income        DOB  \\\n",
       "0  ID000002C20  Female      Delhi           20000  23-May-78   \n",
       "1  ID000004E40    Male     Mumbai           35000   7-Oct-85   \n",
       "2  ID000007H20    Male  Panchkula           22500  10-Oct-81   \n",
       "3  ID000008I30    Male    Saharsa           35000  30-Nov-87   \n",
       "4  ID000009J40    Male  Bengaluru          100000  17-Feb-84   \n",
       "\n",
       "  Lead_Creation_Date  Loan_Amount_Applied  Loan_Tenure_Applied  Existing_EMI  \\\n",
       "0          15-May-15             300000.0                  5.0           0.0   \n",
       "1           4-May-15             200000.0                  2.0           0.0   \n",
       "2          19-May-15             600000.0                  4.0           0.0   \n",
       "3           9-May-15            1000000.0                  5.0           0.0   \n",
       "4          20-May-15             500000.0                  2.0       25000.0   \n",
       "\n",
       "                         Employer_Name    ...    Interest_Rate Processing_Fee  \\\n",
       "0                              CYBOSOL    ...              NaN            NaN   \n",
       "1  TATA CONSULTANCY SERVICES LTD (TCS)    ...            13.25            NaN   \n",
       "2              ALCHEMIST HOSPITALS LTD    ...              NaN            NaN   \n",
       "3                     BIHAR GOVERNMENT    ...              NaN            NaN   \n",
       "4                 GLOBAL EDGE SOFTWARE    ...              NaN            NaN   \n",
       "\n",
       "   EMI_Loan_Submitted Filled_Form  Device_Type  Var2  Source  Var4  LoggedIn  \\\n",
       "0                 NaN           N  Web-browser     G    S122     1         0   \n",
       "1              6762.9           N  Web-browser     G    S122     3         0   \n",
       "2                 NaN           N  Web-browser     B    S143     1         0   \n",
       "3                 NaN           N  Web-browser     B    S143     3         0   \n",
       "4                 NaN           N  Web-browser     B    S134     3         1   \n",
       "\n",
       "  Disbursed  \n",
       "0         0  \n",
       "1         0  \n",
       "2         0  \n",
       "3         0  \n",
       "4         0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv(\"Train1.csv\")\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 87019 entries, 0 to 87018\n",
      "Data columns (total 26 columns):\n",
      "ID                       87019 non-null object\n",
      "Gender                   87019 non-null object\n",
      "City                     86016 non-null object\n",
      "Monthly_Income           87019 non-null int64\n",
      "DOB                      87019 non-null object\n",
      "Lead_Creation_Date       87019 non-null object\n",
      "Loan_Amount_Applied      86948 non-null float64\n",
      "Loan_Tenure_Applied      86948 non-null float64\n",
      "Existing_EMI             86948 non-null float64\n",
      "Employer_Name            86948 non-null object\n",
      "Salary_Account           75255 non-null object\n",
      "Mobile_Verified          87019 non-null object\n",
      "Var5                     87019 non-null int64\n",
      "Var1                     87019 non-null object\n",
      "Loan_Amount_Submitted    52407 non-null float64\n",
      "Loan_Tenure_Submitted    52407 non-null float64\n",
      "Interest_Rate            27726 non-null float64\n",
      "Processing_Fee           27420 non-null float64\n",
      "EMI_Loan_Submitted       27726 non-null float64\n",
      "Filled_Form              87019 non-null object\n",
      "Device_Type              87019 non-null object\n",
      "Var2                     87019 non-null object\n",
      "Source                   87019 non-null object\n",
      "Var4                     87019 non-null int64\n",
      "LoggedIn                 87019 non-null int64\n",
      "Disbursed                87019 non-null int64\n",
      "dtypes: float64(8), int64(5), object(13)\n",
      "memory usage: 17.3+ MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Monthly_Income</th>\n",
       "      <th>Loan_Amount_Applied</th>\n",
       "      <th>Loan_Tenure_Applied</th>\n",
       "      <th>Existing_EMI</th>\n",
       "      <th>Var5</th>\n",
       "      <th>Loan_Amount_Submitted</th>\n",
       "      <th>Loan_Tenure_Submitted</th>\n",
       "      <th>Interest_Rate</th>\n",
       "      <th>Processing_Fee</th>\n",
       "      <th>EMI_Loan_Submitted</th>\n",
       "      <th>Var4</th>\n",
       "      <th>LoggedIn</th>\n",
       "      <th>Disbursed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.701900e+04</td>\n",
       "      <td>8.694800e+04</td>\n",
       "      <td>86948.000000</td>\n",
       "      <td>8.694800e+04</td>\n",
       "      <td>87019.000000</td>\n",
       "      <td>5.240700e+04</td>\n",
       "      <td>52407.000000</td>\n",
       "      <td>27726.000000</td>\n",
       "      <td>27420.000000</td>\n",
       "      <td>27726.000000</td>\n",
       "      <td>87019.000000</td>\n",
       "      <td>87019.000000</td>\n",
       "      <td>87019.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.885053e+04</td>\n",
       "      <td>2.302533e+05</td>\n",
       "      <td>2.131423</td>\n",
       "      <td>3.696270e+03</td>\n",
       "      <td>4.961560</td>\n",
       "      <td>3.950106e+05</td>\n",
       "      <td>3.891369</td>\n",
       "      <td>19.197474</td>\n",
       "      <td>5131.150839</td>\n",
       "      <td>10999.528377</td>\n",
       "      <td>2.949827</td>\n",
       "      <td>0.029350</td>\n",
       "      <td>0.014629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.177524e+06</td>\n",
       "      <td>3.542079e+05</td>\n",
       "      <td>2.014192</td>\n",
       "      <td>3.981044e+04</td>\n",
       "      <td>5.670393</td>\n",
       "      <td>3.082481e+05</td>\n",
       "      <td>1.165359</td>\n",
       "      <td>5.834213</td>\n",
       "      <td>4725.837644</td>\n",
       "      <td>7512.323050</td>\n",
       "      <td>1.697717</td>\n",
       "      <td>0.168786</td>\n",
       "      <td>0.120063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.990000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>1176.410000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.650000e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>15.250000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>6491.600000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.500000e+04</td>\n",
       "      <td>1.000000e+05</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000e+05</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>9392.970000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000e+04</td>\n",
       "      <td>3.000000e+05</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.500000e+03</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>5.000000e+05</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>6250.000000</td>\n",
       "      <td>12919.040000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.445544e+08</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>3.000000e+06</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>144748.280000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Monthly_Income  Loan_Amount_Applied  Loan_Tenure_Applied  Existing_EMI  \\\n",
       "count    8.701900e+04         8.694800e+04         86948.000000  8.694800e+04   \n",
       "mean     5.885053e+04         2.302533e+05             2.131423  3.696270e+03   \n",
       "std      2.177524e+06         3.542079e+05             2.014192  3.981044e+04   \n",
       "min      0.000000e+00         0.000000e+00             0.000000  0.000000e+00   \n",
       "25%      1.650000e+04         0.000000e+00             0.000000  0.000000e+00   \n",
       "50%      2.500000e+04         1.000000e+05             2.000000  0.000000e+00   \n",
       "75%      4.000000e+04         3.000000e+05             4.000000  3.500000e+03   \n",
       "max      4.445544e+08         1.000000e+07            10.000000  1.000000e+07   \n",
       "\n",
       "               Var5  Loan_Amount_Submitted  Loan_Tenure_Submitted  \\\n",
       "count  87019.000000           5.240700e+04           52407.000000   \n",
       "mean       4.961560           3.950106e+05               3.891369   \n",
       "std        5.670393           3.082481e+05               1.165359   \n",
       "min        0.000000           5.000000e+04               1.000000   \n",
       "25%        0.000000           2.000000e+05               3.000000   \n",
       "50%        2.000000           3.000000e+05               4.000000   \n",
       "75%       11.000000           5.000000e+05               5.000000   \n",
       "max       18.000000           3.000000e+06               6.000000   \n",
       "\n",
       "       Interest_Rate  Processing_Fee  EMI_Loan_Submitted          Var4  \\\n",
       "count   27726.000000    27420.000000        27726.000000  87019.000000   \n",
       "mean       19.197474     5131.150839        10999.528377      2.949827   \n",
       "std         5.834213     4725.837644         7512.323050      1.697717   \n",
       "min        11.990000      200.000000         1176.410000      0.000000   \n",
       "25%        15.250000     2000.000000         6491.600000      1.000000   \n",
       "50%        18.000000     4000.000000         9392.970000      3.000000   \n",
       "75%        20.000000     6250.000000        12919.040000      5.000000   \n",
       "max        37.000000    50000.000000       144748.280000      7.000000   \n",
       "\n",
       "           LoggedIn     Disbursed  \n",
       "count  87019.000000  87019.000000  \n",
       "mean       0.029350      0.014629  \n",
       "std        0.168786      0.120063  \n",
       "min        0.000000      0.000000  \n",
       "25%        0.000000      0.000000  \n",
       "50%        0.000000      0.000000  \n",
       "75%        0.000000      0.000000  \n",
       "max        1.000000      1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "查看最小值为0的列，分析为0是否有价值：\n",
    "1. Monthly_Income 月收入，这个为0应该代表示缺失值\n",
    "2. Loan_Amount_Applied - 贷款申请请求金额，这个为0还不确定，要看后面为0的数量，现实意义中这一栏为0说明没有要申请的，如果少的话可以直接剔除这些信息，因为申请贷款为0的话，批与不批都一样\n",
    "3. Loan_Tenure_Applied - 贷款申请期限：同2，这两个值应该是同时为0或者同时不为0，info中看数据是少量缺失\n",
    "4. Existing_EMI -现有贷款的EMI：这一项网上查了不知道什么意思。。从info中查看数据量是缺失严重的，三分之二都没有数据\n",
    "5. Var4-类型型变量：0的话是没有类别，那就可以当0是一个类别"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID                           0\n",
      "Gender                       0\n",
      "City                      1003\n",
      "Monthly_Income             314\n",
      "DOB                          0\n",
      "Lead_Creation_Date           0\n",
      "Loan_Amount_Applied      28923\n",
      "Loan_Tenure_Applied      33914\n",
      "Existing_EMI             58308\n",
      "Employer_Name               71\n",
      "Salary_Account           11764\n",
      "Mobile_Verified              0\n",
      "Var5                         0\n",
      "Var1                         0\n",
      "Loan_Amount_Submitted    34612\n",
      "Loan_Tenure_Submitted    34612\n",
      "Interest_Rate            59293\n",
      "Processing_Fee           59599\n",
      "EMI_Loan_Submitted       59293\n",
      "Filled_Form                  0\n",
      "Device_Type                  0\n",
      "Var2                         0\n",
      "Source                       0\n",
      "Var4                      2546\n",
      "LoggedIn                     0\n",
      "Disbursed                    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "NaN_col_names = ['Monthly_Income','Loan_Amount_Applied','Loan_Tenure_Applied','Existing_EMI','Var4']\n",
    "train[NaN_col_names] = train[NaN_col_names].replace(0, np.NaN)\n",
    "print(train.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到以下4个数据存在大量的缺失，缺失量超过了一半：Existing_EMI,Interest_Rate,Processing_Fee,EMI_Loan_Submitted\n",
    "\n",
    "而以下4个数据存在中等量的缺失，4分之一左右：Loan_Amount_Applied,Loan_Tenure_Applied,Loan_Amount_Submitted,Loan_Tenure_Submitted \n",
    "\n",
    "接下来通过查看这些值的缺失对最后的目标字段Disbursed的影响来决定是新增一个是否缺失的字段还是说直接填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#网上找的改造的seaborn的可视化函数，可以看到具体的值\n",
    "def add_freq():\n",
    "    ncount=len(train)\n",
    "    ax2=ax.twinx()\n",
    "    ax2.yaxis.tick_left()\n",
    "    ax.yaxis.tick_right()\n",
    "    ax.yaxis.set_label_position('right')\n",
    "    ax2.yaxis.set_label_position('left')\n",
    "    ax2.set_ylabel('Frequency [%]')\n",
    "    for p in ax.patches:\n",
    "        x=p.get_bbox().get_points()[:,0]\n",
    "        y=p.get_bbox().get_points()[1,1]\n",
    "        ax.annotate('{:.1f}%'.format(100.*y/ncount),(x.mean(),y),ha='center',va='bottom')\n",
    "    ax2.set_ylim(0,100)\n",
    "    ax2.grid(None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Existing_EMI</th>\n",
       "      <th>Existing_EMI_Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2597.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Existing_EMI  Existing_EMI_Missing\n",
       "0           NaN                     1\n",
       "1           NaN                     1\n",
       "2           NaN                     1\n",
       "3           NaN                     1\n",
       "4       25000.0                     0\n",
       "5       15000.0                     0\n",
       "6           NaN                     1\n",
       "7        2597.0                     0\n",
       "8           NaN                     1\n",
       "9           NaN                     1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Existing_EMI_Missing'] = train['Existing_EMI'].apply(lambda x: 1 if pd.isnull(x) else 0)\n",
    "train[['Existing_EMI','Existing_EMI_Missing']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.countplot(x=\"Existing_EMI_Missing\", hue=\"Disbursed\",data=train)\n",
    "add_freq()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.countplot(x=\"Existing_EMI_Missing\", hue=\"Disbursed\",data=train)\n",
    "add_freq()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EMI_Loan_Submitted</th>\n",
       "      <th>EMI_Loan_Submitted_Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6762.90</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6978.92</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>30824.65</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10883.38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   EMI_Loan_Submitted  EMI_Loan_Submitted_Missing\n",
       "0                 NaN                           1\n",
       "1             6762.90                           0\n",
       "2                 NaN                           1\n",
       "3                 NaN                           1\n",
       "4                 NaN                           1\n",
       "5             6978.92                           0\n",
       "6                 NaN                           1\n",
       "7                 NaN                           1\n",
       "8            30824.65                           0\n",
       "9            10883.38                           0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['EMI_Loan_Submitted_Missing'] = train['EMI_Loan_Submitted'].apply(lambda x: 1 if pd.isnull(x) else 0)\n",
    "train[['EMI_Loan_Submitted','EMI_Loan_Submitted_Missing']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.countplot(x=\"EMI_Loan_Submitted_Missing\", hue=\"Disbursed\",data=train)\n",
    "add_freq()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Interest_Rate</th>\n",
       "      <th>Interest_Rate_Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14.85</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>18.25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Interest_Rate  Interest_Rate_Missing\n",
       "0            NaN                      1\n",
       "1          13.25                      0\n",
       "2            NaN                      1\n",
       "3            NaN                      1\n",
       "4            NaN                      1\n",
       "5          13.99                      0\n",
       "6            NaN                      1\n",
       "7            NaN                      1\n",
       "8          14.85                      0\n",
       "9          18.25                      0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Interest_Rate_Missing'] = train['Interest_Rate'].apply(lambda x: 1 if pd.isnull(x) else 0)\n",
    "train[['Interest_Rate','Interest_Rate_Missing']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HH1FcXMyNN97IRRddVOtx7MxqPclFxMuSHiJ5NrsBeJ3k8eOfgRJJN6dl99Z2bGZm9cnEiRPrOoSdXp10Bo+IkSS94DO9S9J6x8zMrEZ4WC8zMytYTnJm1mDVRReq2lTo15cLJzkza5CaNWvGxx9/XLCJICL4+OOPt6svYCHyAM1m1iAVFxdTWlrKhx9+WNeh5E2zZs0oLi7edsUC5iRnZg1SkyZN6NChQ12HYXnmx5VmZlawnOTMzKxgOcmZmVnBcpIzM7OC5SRnZmYFy0nOzMwKlpOcmZkVLCc5MzMrWE5yZmZWsJzkzMysYDnJmZlZwXKSMzOzguUkZ2ZmBctJzszMCpaTnJmZFSwnOTMzK1hOcmZmVrCc5MzMrGA5yZmZ2VYkFUl6XdJj6XoHSS9LWijpQUlN0/Jd0vVF6fb2Gce4Oi1/R9LAjPJBadkiSSPyeR2NK9sgqUcO+6+PiLdqMB4zM6sfLgMWAHuk6/8J3BERJZJ+BVwEjE3/fhoR+0k6M613hqQuwJnAgcC+wHRJ30yPdTcwACgFXpE0JSLm5+MiKk1ywHPAK4CqqNMBaF+TAZmZWd2SVAycANwCDJck4Cjg+2mV8cANJEnu5HQZ4CHgrrT+yUBJRHwBLJa0CDg0rbcoIt5Nz1WS1q31JPdKRBxV1c6SnqnheMzMrO79Evg5sHu6viewIiI2pOulQJt0uQ3wPkBEbJC0Mq3fBngp45iZ+7y/RflhNX0B5Sp9J7etBJdrHTMzq3caS5qT8RlavkHSicDyiHg1o362J3qxjW3bW54XVd3JbUbSXiTPaHcFxkbEonwFZWZmebUhInpVsq0vMFjS8UAzkndyvwRaSmqc3s0VA0vT+qVAW6BUUmPgK8AnGeXlMveprLzGbU/rytuBmcBUYGJ+wjEzs7oUEVdHRHFEtCdpOPJMRJwNPAucmlYbAkxOl6ek66Tbn4mISMvPTFtfdgA6AbNJ2np0SltrNk3PMSVf11NpkpM0VdK3MoqaAu+ln13yFZCZmdVLV5E0QllE8s7t3rT8XmDPtHw4MAIgIv4CTCJpUDIVGBYRG9M7wR8BT5K03pyU1s0LJQk3ywbpK8B1JE0/ryNJiCNJHlfeEREv5CuoXLVo0SJWr15d12GY5UXPKyfUdQj1xqu3nVfXIRQUSWUR0aKu46gNlb6Ti4iVwM8kdSRpRvoBSSZeWVvBmZmZ7YiqOoN3BH4ArAeuAL4BTEp7v/9PRGysnRDNzMyqp6qGJxNJnqO+BDwQEc9HxEBgFfDUjpxUUktJD0l6W9ICSX0kfVXStHTImGmSWu3IOczMzKpKcs2AxemneXlhRIwHTtzB894JTI2IA4BDSF4+jgCejohOwNPpupmZWbVV1U/uB8BtwDrg0swNEbGmuieUtAdwJHB+eqx1wDpJJwP90mrjgRkkrXnMzMyqpaqGJ/8H/F8eztkR+BD4jaRDgFdJOpnvHRHL0nMvk9Q6285pz/yhAK1atWLGjBl5CNGs7l18SINo/JYT/3du1VVVF4JxETE068btqJNln14k7/n6RsTLku4kec/344homVHv04io8r2cuxBYIXMXgi+5C0HNcheCxHckra1iu4D+1ThnKVAaES+n6w+RvH/7p6R90ru4fYDl1Ti2mZlZhaqS3JU57P/89p4wIv4h6X1J+0fEO8DRJD3i55MMDXMrmw8ZY2ZmVi1VvZMbn8fz/hj4XTpu2bvABSQtPSdJughYApyWx/ObmVkDkPMsBDUpIuYC2UbAPrq2YzEzs8K1PbMQmJmZ7VS2meQkHVQbgZiZmdW0XO7kfiVptqQfSmq57epmZmb1wzaTXEQcAZxNMpPrHEm/lzQg75GZmZntoJzeyUXEQuDfSIbZ+jYwOh1c+bv5DM7MzGxH5PJOrqukO0gGUT4KOCkiOqfLd+Q5PjMza+AkPZ1LWTa5dCG4C/hf4JrMgZkjYqmkf8s5SjMzs+0gqRnJLDhfS6dfU7ppD2DfXI6RS5I7HlhTPkmqpEZAs4goi4gHtj9sMzOznPw/4HKShPYqXya5VcDduRwgl3dy04FdM9abp2VmZmZ5ExF3RkQH4GcR0TEiOqSfQyLirlyOkcudXLOI+DzjpJ9Lal7VDmZmZjUlIsZI+legPRl5KyK2OVVHLklutaQeEfEagKSeQLUnTTUzM9sekh4AvgHMBTamxQHUSJK7HPiDpKXp+j7AGdWI08zMrDp6AV2isglQq7DNJBcRr0g6ANif5KXf2xGxfvtjNDMzq5Z5wL8Ay7Z3x1xnIejNl89Cu0vK6VmomZlZDfgaMF/SbOCL8sKIGLytHbeZ5HbkWaiZmVkNuKG6O+ZyJ1ftZ6FmZmY7KiKeq+6+ufSTK38WamZmDYCkZunsM29I+oukG9PyDpJelrRQ0oOSmqblu6Tri9Lt7TOOdXVa/o6kgRnlg9KyRZJGbCOezyStSj9rJW2UtCqXa8nlTq7az0LNzGyn9AVwVNovugnwgqQngOHAHRFRIulXwEXA2PTvpxGxn6Qzgf8EzpDUBTgTOJBk1JLpkr6ZnuNuYABQCrwiaUpEzM8WTETsnrku6TvAoblcSC5J7oZcDmRmZoUhfT1VPghIk/QTJAPzfz8tH0+SH8YCJ/NlrngIuEuS0vKSiPgCWCxpEV8mp0UR8S6ApJK0btYklyW+P27r7q9cLl0InpP0daBTRExPRzspyuXgZmZWLzWWNCdjfVxEjMusIKmIZLzI/Ujuuv4GrIiIDWmVUqBNutwGeB8gIjZIWgnsmZa/lHHYzH3e36L8sMqC3WJat0YkbUVyaieSy1Q7l5Bk5nvSojbAH3M5eEOydu1aDj30UA455BAOPPBARo4cCcBdd93FfvvthyQ++uijrPvOnTuXPn36cOCBB9K1a1cefPDBim1nn302Xbt25ZprrqkoGzVqFJMnT87vBZlZIdsQEb0yPuO2rBARGyOiG1BMcvfVOctxyhONKtm2veWVOSnjMxD4jOTOb5tyeVw5jOQCX4ZkAlVJrXM5eEOyyy678Mwzz7Dbbruxfv16jjjiCI477jj69u3LiSeeSL9+/Srdt3nz5kyYMIFOnTqxdOlSevbsycCBA1myZAkAb775Jt/61rdYuXIlZWVlzJ49m+uuu66WrszMGrKIWCFpBnA40FJS4/RurhgoHwmrFGgLlEpqDHwF+CSjvFzmPpWVZ4vhgurGn0vryi8iYl35SnoB7k6wBUnstttuAKxfv57169cjie7du9O+ffsq9/3mN79Jp06dANh3331p3bo1H374IU2aNGHNmjVs2rSJdevWUVRUxPXXX89NN92U78sxswZM0l6SWqbLuwLHkEyc/SxwalptCFD+SGlKuk66/Zn0vd4U4My09WUHoBMwG3gF6JS21mxK0jhlShXxFEt6VNJySf+U9LCk4lyuJZck95yka4BdJQ0A/gD8KZeDNzQbN26kW7dutG7dmgEDBnDYYZU+Yq7U7NmzWbduHd/4xjfo3Lkz7dq1o0ePHpx++uksWrSIiKB79+55iN7MrMI+wLOS3iRJSNMi4jHgKmB42oBkT+DetP69wJ5p+XBgBEBE/AWYRNKgZCowLH0MugH4EfAkSfKclNatzG9IkuC+JK/M/pSWbZO21cc7nST1IuBYkueoTwK/rg+dw1u0aBGrV6+u6zC2smLFCk455RTGjBnDQQcdBED79u2ZM2cOX/va1yrdb9myZfTr14/x48dz+OGHb7X9pJNO4p577uE3v/kNb7zxBgMGDOCSSy7J23VY3ep5pQcVKvfqbefVdQgFRVJZRLSo6zhyJWlu+n6wyrJstnknFxGbIuJ/I+K0iDg1Xa7zBFeftWzZkn79+jF16tSc91m1ahUnnHACN998c9YEN3nyZHr16sXq1auZN28ekyZN4oEHHqCsrKwmQzczq48+knSOpKL0cw7wcS475tK6crGkd7f87HDIBebDDz9kxYoVAKxZs4bp06dzwAEH5LTvunXrOOWUUzjvvPM47bTTttq+fv167rzzTq688krKyspIup9Q8a7OzKzAXQicDvyDZCaCU4GcGqPk8k6uF8ksBL2BbwGjgd9WK8wCtmzZMvr370/Xrl3p3bs3AwYM4MQTT2T06NEUFxdTWlpK165dufjiiwGYM2dOxfKkSZOYOXMm999/P926daNbt27MnTu34th33303Q4YMoXnz5nTt2pWI4OCDD6Zv3760bNmyTq7XzKwWjQKGRMReEdGaJOndkMuO23wnl3Un6YWIOGK7d6xh9fWdnFlN8Du5L/mdXM3aCd/JvR4R3bdVlk0uU+30yFgt72m+eyXVdzr+IfmSf0jMrJ5qJKlVRHwKIOmr5Dgfai6Vbs9Y3gC8R/Js1MzMrDbcDvyfpIdI+mmfDtySy465jF3Zf8diMzMzq76ImJCOtXkUSVe271Y2Y8GWcnlcOXwbJ//vnKI0MzOrpjSp5ZTYMuU6M3hvvhxy5SRgJpuPIG1mZlbv5Dppao+I+AxA0g3AHyLi4nwGZmZmtqNy6SfXDsjscbwOaL+jJ057rb8u6bF0Peu06mZmZtWVS5J7AJgt6QZJI0mm3KmJdveXkQzMWe4/SaZV7wR8SjJeppmZWbXlMnblLSTDp3wKrAAuiIh/35GTplMknAD8Ol0XSauZh9Iq44Hv7Mg5zMzMcupMBzQHVkXEb9J5hjpExOIdOO8vgZ/zZafyPal8WvXNSBoKDAVo1aoVM2bM2IEw4OJDdppO/3m3o/+WVrP83fySv5tWXbl0IRhJ0sJyf5L5e5qQjF3ZtzonlHQisDwiXpXUr7w4S9Ws442l07SPg2RYr6pm3M7FFR7xpMKr53yvrkOwDP5ufsnfTauuXO7kTgG6A68BRMRSSTsyrFdfYLCk44FmwB4kd3aVTatuZmZWLbk0PFmXzh8XAJJ26BlKRFwdEcUR0Z5kyvNnIuJsKp9W3czMrFpySXKTJN1Dcqd1CTAd+N88xFLZtOpmZmbVksvYlb+QNABYRfJe7vqImFYTJ4+IGcCMdPld4NCaOK6ZmRlsI8lJKgKejIhjgBpJbGZmZrWlyseVEbERKJP0lVqKx8zMrMbk0rpyLfCWpGlAxTTcEfGTvEVlZmZWA3JJcn9OP2ZmZjuVSpOcpHYRsSQixtdmQGZmZjWlqndyfyxfkPRwLcRiZmZWo6pKcplDbXXMdyBmZmY1raokF5Usm5lZAZPUVtKzkhZI+ouky9Lyr0qals77OU1Sq7RckkZLWiTpTUk9Mo41JK2/UNKQjPKekt5K9xmdzkZT46pKcodIWiXpM6BrurxK0meSVuUjGDMzqxc2AFdERGfgcGCYpC7ACODpdN7Pp9N1gOOATulnKDAWkqQIjAQOIxnsY2R5YkzrDM3Yb1A+LqTSJBcRRRGxR0TsHhGN0+Xy9T3yEYyZmdW9iFgWEeWD8n9GMsF1G+Bkkvk+YfN5P08GJkTiJZJhIPcBBgLTIuKTiPiUZFCRQem2PSJiVjo28gTyNIdorvPJmZlZ4WgsaU7G+rh0GrOtSGpPMhPNy8DeEbEMkkQoqXVarQ3wfsZu5XOCVlVemqW8xjnJmZk1PBsiote2KknaDXgYuDwiVlXx2qyyOUG3t7zG5TILgZmZNTCSmpAkuN9FxCNp8T/TR42kf5en5aVA24zdy+cEraq8OEt5jXOSMzOzzaQtHe8FFkTEf2dsmkIy3ydsPu/nFOC8tJXl4cDK9LHmk8CxklqlDU6OJRn0fxnwmaTD03OdR57mEPXjSjMz21Jf4FyScYvnpmXXALeSzDF6EbAEOC3d9jhwPLAIKAMuAIiITySNAl5J690UEZ+kyz8A7gd2BZ5IPzXOSc7MzDYTES+Q/b0ZwNFZ6gcwrJJj3Qfcl6V8DnDQDoSZEz+uNDOzguUkZ2ZmBctJzszMCpaTnJmZFSwnOTMzK1hOcmZmVrCc5MzMrGA5yZmZWcFykjMzs4LlJGdmZgXLSc7MzAqWk5yZmRUsJzkzMytYTnJmZlawnOTMzKxgOcmZmVnBcpIzM7OC5SRnZmYFq9aTnKS2kp6VtEDSXyRdlpZ/VdI0SQvTv61qOzYzMyssdXEntwG4IiI6A4cDwyR1AUYAT0dEJ+DpdN3MzKzaaj3JRcSSvLpHAAANsElEQVSyiHgtXf4MWAC0AU4GxqfVxgPfqe3YzMyssCgi6u7kUntgJnAQsCQiWmZs+zQitnpkKWkoMBSgVatWPR955JEdimFB6cc7tH8h6Vy8Z12HYBn83fySv5s1q3///mUR0aKu46gNdZbkJO0GPAfcEhGPSFqRS5LL1KJFi1i9evUOxdHzygk7tH8hefW28+o6BMvg7+aX/N2sWZIaTJKrk9aVkpoADwO/i4jyW7F/Ston3b4PsLwuYjMzs8JRF60rBdwLLIiI/87YNAUYki4PASbXdmxmZlZYGtfBOfsC5wJvSZqbll0D3ApMknQRsAQ4rQ5iMzOzAlIXrStfiAhFRNeI6JZ+Ho+IjyPi6IjolP79pLZjMzMzkHSfpOWS5mWUZe3LrMRoSYskvSmpR8Y+Q9L6CyUNySjvKemtdJ/R6RO+vPCIJ2ZmtqX7gUFblFXWl/k4oFP6GQqMhSQpAiOBw4BDgZEZg3yMTeuW77fluWqMk5yZmW0mImYCWz5Nq6wv88nAhEi8BLRMGw8OBKZFxCcR8SkwDRiUbtsjImZF0rx/AnnsF+0kZ2Zmudg7IpZBMqgH0DotbwO8n1GvNC2rqrw0S3le1EXDEzMzq1uNJc3JWB8XEeOqeaxs79OiGuV54SRnZtbwbIiIXtu5zz8l7RMRy7boy1wKtM2oVwwsTcv7bVE+Iy0vzlI/L/y40szMclFZX+YpwHlpK8vDgZXp48wngWMltUobnBwLPJlu+0zS4WmryvPIY79o38mZmdlmJE0kuQv7mqRSklaSlfVlfhw4HlgElAEXAETEJ5JGAa+k9W7K6Br2A5IWnLsCT6SfvHCSMzOzzUTEWZVsOjpL3QCGVXKc+4D7spTPIRmYP+/8uNLMzAqWk5yZmRUsJzkzMytYTnJmZlawnOTMzKxgOcmZmVnBcpIzM7OC5SRnZmYFy0nOzMwKlpOcmZkVLCc5MzMrWE5yZmZWsJzkzMysYDnJmZlZwXKSMzOzguUkZ2ZmBctJzszMCpaTnJmZFSwnOTNr0KZOncr+++/Pfvvtx6233rrV9iVLltC/f3+6d+9O165defzxxwF48cUX6dq1K71792bRokUArFixgoEDBxIRtXoNVjknOcu7bf2I/PSnP6Vbt25069aNb37zm7Rs2RKAd955h549e3LIIYcwa9YsADZs2MAxxxxDWVlZrV6DFaaNGzcybNgwnnjiCebPn8/EiROZP3/+ZnVuvvlmTj/9dF5//XVKSkr44Q9/CMDtt9/Oww8/zL//+78zduxYAEaNGsU111yDpFq/FsuucV0HYIWt/Edk2rRpFBcX07t3bwYPHkyXLl0q6txxxx0Vy2PGjOH1118H4J577uHWW2+lffv2jBgxgocffpixY8dy7rnn0rx581q/Fis8s2fPZr/99qNjx44AnHnmmUyePHmz76ckVq1aBcDKlSvZd999AWjSpAlr1qyhrKyMJk2a8Le//Y0PPviAb3/727V/IVYpJznLq1x+RDJNnDiRG2+8Edj6R2TFihX86U9/4sknn6y1+K2wffDBB7Rt27Zivbi4mJdffnmzOjfccAPHHnssY8aMYfXq1UyfPh2Aq6++mqFDh7LrrrvywAMP8LOf/YxRo0bVavy2bU5ylle5/IiU+/vf/87ixYs56qijABg2bBjnnXceX3zxBffccw833XQT1157rR8FWY3J9u5sy+/XxIkTOf/887niiiuYNWsW5557LvPmzaNbt2689NJLAMycOZN9992XiOCMM86gSZMm3H777ey99961ch1WOb+Ts7zK5UekXElJCaeeeipFRUUAtGvXjhkzZjBr1iyaN2/O0qVLOeCAAzj33HM544wz+Otf/5rX2K3wFRcX8/7771esl5aWVjyOLHfvvfdy+umnA9CnTx/Wrl3LRx99VLE9Irj55pu57rrruPHGG7nxxhs555xzGD16dO1chFXJSc7yKpcfkXIlJSWcddZZWbdde+21jBo1itGjR3P22WdX/JiY7YjevXuzcOFCFi9ezLp16ygpKWHw4MGb1WnXrh1PP/00AAsWLGDt2rXstddeFdvHjx/PCSecQKtWrSgrK6NRo0Y0atTIjaPqCT+utLzK/BFp06YNJSUl/P73v9+q3jvvvMOnn35Knz59ttr23HPP0aZNGzp16lTxI1JUVOQfEdthjRs35q677mLgwIFs3LiRCy+8kAMPPJDrr7+eXr16MXjwYG6//XYuueQS7rjjDiRx//33VzyNKCsrY/z48Tz11FMADB8+nO9973s0bdqUiRMn1uWlWUo7c3+OFi1axOrVq3foGD2vnFBD0ez8Xr3tvLwc9/HHH+fyyy+v+BG59tprN/sRgeTl/tq1a7fqYhARHHvssUyaNIlWrVqxYMECzj77bDZs2MDYsWPp27dvXmKuD/zd/FK+vpsNlaSyiGhR13HUhnqV5CQNAu4EioBfR8TWnaoyOMnVLP+Q1C/+bn7p0d1vq+sQ6o1217+1w8fIJclt7+9xfVVvHldKKgLuBgYApcArkqZExPyq97SasuSmg+s6hHqjJn5IzHZWhfR7XJ8anhwKLIqIdyNiHVACnFzHMZmZNUQF83tcb+7kgDbA+xnrpcBhW1aSNBQYmq6GpDW1EFuD8PXk+7ChruOoF0a6L1594u9mhpr5bu4qaU7G+riIGJexntPv8c6gPiW5bP/LbfXCMP0fYlyWuraDJM2JiF51HYfZlvzdrHU5/R7vDOrT48pSoG3GejGwtI5iMTNryArm97g+JblXgE6SOkhqCpwJTKnjmMzMGqKC+T2uN48rI2KDpB8BT5I0Wb0vIv5Sx2E1NH4MbPWVv5u1qJB+j+tVPzkzM7OaVJ8eV5qZmdUoJzkzMytYTnKGpEGS3pG0SNKIuo7HrJyk+yQtlzSvrmOxnZOTXAOXMXzPcUAX4CxJ2aftNqt99wOD6joI23k5yVnBDN9jhSciZgKf1HUctvNykrNsw/e0qaNYzMxqlJOcFczwPWZmW3KSs4IZvsfMbEtOclYww/eYmW3JSa6Bi4gNQPnwPQuASTvr8D1WeCRNBGYB+0sqlXRRXcdkOxcP62VmZgXLd3JmZlawnOTMzKxgOcmZmVnBcpIzM7OC5SRnZmYFy0nOzMwKlpOc1TlJn+dQ53JJzfMcRzdJx2+jzvmSPpQ0V9Lbkn6aw3H7SfrXasZ0vqSQdHRG2Slp2anp+q+3d+YISYM9rZI1BE5ytrO4HNiuJJdOI7Q9ugFVJrnUgxHRDegLXCup7Tbq9wOqleRSbwFnZayfCbxRvhIRF0fE/O05YERMiYhbdyAms52Ck5zVG+kdzwxJD6V3Sb9T4ifAvsCzkp5N6x4raZak1yT9QdJuafl7kq6X9AJwmqRvSJoq6VVJz0s6IK13mqR5kt6QNDMd0uwm4Iz0Lu2MbcUbER8Di4B90mOeJOllSa9Lmi5pb0ntgUuBn6bH/ZakvSQ9LOmV9NN3G6d6HjhUUpP0OvcD5mb8u82Q1EtSkaT70+t6q/wuU9JPJM2X9KakkrTsfEl3pcv3Sxot6f8kvZtxh9hI0v9I+oukxyQ9Xr7NbGfRuK4DMNtCd+BAkkGiXwT6RsRoScOB/hHxkaSvAf8GHBMRqyVdBQwnSVIAayPiCABJTwOXRsRCSYcB/wMcBVwPDIyIDyS1jIh1kq4HekXEj3IJVFI7oBnwZlr0AnB4RISki4GfR8QVkn4FfB4Rv0j3+z1wR0S8kB7jSaBzFacKYDowEPgKydiiHbLU6wa0iYiD0vO0TMtHAB0i4ouMsi3tAxwBHJAe/yHgu0B74GCgNcmwb/dVEadZveMkZ/XN7IgoBZA0l+RH9oUt6hxOMov5i5IAmpKMb1juwXT/3UgeE/4hrQewS/r3ReB+SZOAR7YzxjMk9Qf2By6JiLVpeTHwoKR90pgWV7L/MUCXjJj2kLR7RHxWxTlLgJ+QJLkrgGuy1HkX6ChpDPBn4Km0/E3gd5L+CPyxkuP/MSI2AfMl7Z2WHQH8IS3/R/ldtNnOxEnO6psvMpY3kv07KmBaRJyVZRvA6vRvI2BF+v5sMxFxaXpndwIwV9JWdarwYET8SFIf4M+SnoiIfwBjgP+OiCmS+gE3VLJ/I6BPRKzJ9YQRMVvSQcCaiPhrRoLMrPOppENI7viGAacDF5Jc45HAYOA6SQdmOUXmv7u2+Gu20/I7OdtZfAbsni6/BPSVtB+ApOaSvrnlDhGxClgs6bS0ntIkgKRvRMTLEXE98BHJnHqZ59imiJgFPABclhZ9BfggXR5SSeyQ3GFVPBLdjgR7Ndnv4MqP8zWgUUQ8DFwH9JDUCGgbEc8CPwdaArvleL4XgO+l7+b2JmlAY7ZTcZKzncU44AlJz0bEh8D5wERJb5IkvQMq2e9s4CJJbwB/AU5Oy29LG2fMA2aStFZ8luQxYk4NT1L/CVwgaXeSO7c/SHqeJHGW+xNwSnnDE5LHjr3ShiDzSRqmbFNEPJEmq8q0AWakj3nvJ0mKRcBvJb0FvE7yLnBFjtf2MMmkuvOAe4CXgZU57mtWL3iqHTOrlKTdIuJzSXsCs0kaAv2jruMyy5XfyZlZVR5LW2Q2BUY5wdnOxndyZllIuoAv37WVezEihhXC+cwaCic5MzMrWG54YmZmBctJzszMCpaTnJmZFSwnOTMzK1j/H+MZtM7CiLTsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.countplot(x=\"Interest_Rate_Missing\", hue=\"Disbursed\",data=train)\n",
    "add_freq()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Processing_Fee</th>\n",
       "      <th>Processing_Fee_Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1500.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>26000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1500.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Processing_Fee  Processing_Fee_Missing\n",
       "0             NaN                       1\n",
       "1             NaN                       1\n",
       "2             NaN                       1\n",
       "3             NaN                       1\n",
       "4             NaN                       1\n",
       "5          1500.0                       0\n",
       "6             NaN                       1\n",
       "7             NaN                       1\n",
       "8         26000.0                       0\n",
       "9          1500.0                       0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Processing_Fee_Missing'] = train['Processing_Fee'].apply(lambda x: 1 if pd.isnull(x) else 0)\n",
    "train[['Processing_Fee','Processing_Fee_Missing']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.countplot(x=\"Processing_Fee_Missing\", hue=\"Disbursed\",data=train)\n",
    "add_freq()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从图中可以分析得Existing_EMI,Interest_Rate,Processing_Fee,EMI_Loan_Submitted这4个字段是否缺失对结果影响不是很大，所以不需要新开缺失列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_Amount_Applied</th>\n",
       "      <th>Loan_Amount_Applied_Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>300000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>600000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>500000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>300000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>200000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>300000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Loan_Amount_Applied  Loan_Amount_Applied_Missing\n",
       "0             300000.0                            0\n",
       "1             200000.0                            0\n",
       "2             600000.0                            0\n",
       "3            1000000.0                            0\n",
       "4             500000.0                            0\n",
       "5             300000.0                            0\n",
       "6                  6.0                            0\n",
       "7             200000.0                            0\n",
       "8                  NaN                            1\n",
       "9             300000.0                            0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Loan_Amount_Applied_Missing'] = train['Loan_Amount_Applied'].apply(lambda x: 1 if pd.isnull(x) else 0)\n",
    "train[['Loan_Amount_Applied','Loan_Amount_Applied_Missing']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.countplot(x=\"Loan_Amount_Applied_Missing\", hue=\"Disbursed\",data=train)\n",
    "add_freq()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_Tenure_Applied</th>\n",
       "      <th>Loan_Tenure_Applied_Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Loan_Tenure_Applied  Loan_Tenure_Applied_Missing\n",
       "0                  5.0                            0\n",
       "1                  2.0                            0\n",
       "2                  4.0                            0\n",
       "3                  5.0                            0\n",
       "4                  2.0                            0\n",
       "5                  5.0                            0\n",
       "6                  5.0                            0\n",
       "7                  5.0                            0\n",
       "8                  NaN                            1\n",
       "9                  3.0                            0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Loan_Tenure_Applied_Missing'] = train['Loan_Tenure_Applied'].apply(lambda x: 1 if pd.isnull(x) else 0)\n",
    "train[['Loan_Tenure_Applied','Loan_Tenure_Applied_Missing']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.countplot(x=\"Loan_Tenure_Applied_Missing\", hue=\"Disbursed\",data=train)\n",
    "add_freq()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_Amount_Submitted</th>\n",
       "      <th>Loan_Amount_Submitted_Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>450000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>920000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>500000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>300000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>200000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1300000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>300000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Loan_Amount_Submitted  Loan_Amount_Submitted_Missing\n",
       "0                    NaN                              1\n",
       "1               200000.0                              0\n",
       "2               450000.0                              0\n",
       "3               920000.0                              0\n",
       "4               500000.0                              0\n",
       "5               300000.0                              0\n",
       "6                    NaN                              1\n",
       "7               200000.0                              0\n",
       "8              1300000.0                              0\n",
       "9               300000.0                              0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Loan_Amount_Submitted_Missing'] = train['Loan_Amount_Submitted'].apply(lambda x: 1 if pd.isnull(x) else 0)\n",
    "train[['Loan_Amount_Submitted','Loan_Amount_Submitted_Missing']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.countplot(x=\"Loan_Amount_Submitted_Missing\", hue=\"Disbursed\",data=train)\n",
    "add_freq()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_Tenure_Submitted</th>\n",
       "      <th>Loan_Tenure_Submitted_Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Loan_Tenure_Submitted  Loan_Tenure_Submitted_Missing\n",
       "0                    NaN                              1\n",
       "1                    2.0                              0\n",
       "2                    4.0                              0\n",
       "3                    5.0                              0\n",
       "4                    2.0                              0\n",
       "5                    5.0                              0\n",
       "6                    NaN                              1\n",
       "7                    5.0                              0\n",
       "8                    5.0                              0\n",
       "9                    3.0                              0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Loan_Tenure_Submitted_Missing'] = train['Loan_Tenure_Submitted'].apply(lambda x: 1 if pd.isnull(x) else 0)\n",
    "train[['Loan_Tenure_Submitted','Loan_Tenure_Submitted_Missing']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.countplot(x=\"Loan_Tenure_Submitted_Missing\", hue=\"Disbursed\",data=train)\n",
    "add_freq()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从图中可以分析得Loan_Amount_Applied,Loan_Tenure_Applied,Loan_Amount_Submitted,Loan_Tenure_Submitted 这4个字段是否缺失对结果影响不是很大，所以也不需要新开缺失列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop([\"Existing_EMI_Missing\", \"Interest_Rate_Missing\",\"Processing_Fee_Missing\",\"EMI_Loan_Submitted_Missing\",\n",
    "            \"Loan_Amount_Applied_Missing\",\"Loan_Tenure_Applied_Missing\",\"Loan_Amount_Submitted_Missing\",\"Loan_Tenure_Submitted_Missing\"], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>City</th>\n",
       "      <th>Monthly_Income</th>\n",
       "      <th>DOB</th>\n",
       "      <th>Lead_Creation_Date</th>\n",
       "      <th>Loan_Amount_Applied</th>\n",
       "      <th>Loan_Tenure_Applied</th>\n",
       "      <th>Existing_EMI</th>\n",
       "      <th>Employer_Name</th>\n",
       "      <th>...</th>\n",
       "      <th>Interest_Rate</th>\n",
       "      <th>Processing_Fee</th>\n",
       "      <th>EMI_Loan_Submitted</th>\n",
       "      <th>Filled_Form</th>\n",
       "      <th>Device_Type</th>\n",
       "      <th>Var2</th>\n",
       "      <th>Source</th>\n",
       "      <th>Var4</th>\n",
       "      <th>LoggedIn</th>\n",
       "      <th>Disbursed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID000002C20</td>\n",
       "      <td>Female</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>20000</td>\n",
       "      <td>23-May-78</td>\n",
       "      <td>15-May-15</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>CYBOSOL</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>Web-browser</td>\n",
       "      <td>G</td>\n",
       "      <td>S122</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID000004E40</td>\n",
       "      <td>Male</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>35000</td>\n",
       "      <td>7-Oct-85</td>\n",
       "      <td>4-May-15</td>\n",
       "      <td>200000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TATA CONSULTANCY SERVICES LTD (TCS)</td>\n",
       "      <td>...</td>\n",
       "      <td>13.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6762.9</td>\n",
       "      <td>N</td>\n",
       "      <td>Web-browser</td>\n",
       "      <td>G</td>\n",
       "      <td>S122</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID000007H20</td>\n",
       "      <td>Male</td>\n",
       "      <td>Panchkula</td>\n",
       "      <td>22500</td>\n",
       "      <td>10-Oct-81</td>\n",
       "      <td>19-May-15</td>\n",
       "      <td>600000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ALCHEMIST HOSPITALS LTD</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>Web-browser</td>\n",
       "      <td>B</td>\n",
       "      <td>S143</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID000008I30</td>\n",
       "      <td>Male</td>\n",
       "      <td>Saharsa</td>\n",
       "      <td>35000</td>\n",
       "      <td>30-Nov-87</td>\n",
       "      <td>9-May-15</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>BIHAR GOVERNMENT</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>Web-browser</td>\n",
       "      <td>B</td>\n",
       "      <td>S143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID000009J40</td>\n",
       "      <td>Male</td>\n",
       "      <td>Bengaluru</td>\n",
       "      <td>100000</td>\n",
       "      <td>17-Feb-84</td>\n",
       "      <td>20-May-15</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>GLOBAL EDGE SOFTWARE</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>Web-browser</td>\n",
       "      <td>B</td>\n",
       "      <td>S134</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID  Gender       City  Monthly_Income        DOB  \\\n",
       "0  ID000002C20  Female      Delhi           20000  23-May-78   \n",
       "1  ID000004E40    Male     Mumbai           35000   7-Oct-85   \n",
       "2  ID000007H20    Male  Panchkula           22500  10-Oct-81   \n",
       "3  ID000008I30    Male    Saharsa           35000  30-Nov-87   \n",
       "4  ID000009J40    Male  Bengaluru          100000  17-Feb-84   \n",
       "\n",
       "  Lead_Creation_Date  Loan_Amount_Applied  Loan_Tenure_Applied  Existing_EMI  \\\n",
       "0          15-May-15             300000.0                  5.0           0.0   \n",
       "1           4-May-15             200000.0                  2.0           0.0   \n",
       "2          19-May-15             600000.0                  4.0           0.0   \n",
       "3           9-May-15            1000000.0                  5.0           0.0   \n",
       "4          20-May-15             500000.0                  2.0       25000.0   \n",
       "\n",
       "                         Employer_Name    ...    Interest_Rate Processing_Fee  \\\n",
       "0                              CYBOSOL    ...              NaN            NaN   \n",
       "1  TATA CONSULTANCY SERVICES LTD (TCS)    ...            13.25            NaN   \n",
       "2              ALCHEMIST HOSPITALS LTD    ...              NaN            NaN   \n",
       "3                     BIHAR GOVERNMENT    ...              NaN            NaN   \n",
       "4                 GLOBAL EDGE SOFTWARE    ...              NaN            NaN   \n",
       "\n",
       "   EMI_Loan_Submitted Filled_Form  Device_Type  Var2  Source  Var4  LoggedIn  \\\n",
       "0                 NaN           N  Web-browser     G    S122     1         0   \n",
       "1              6762.9           N  Web-browser     G    S122     3         0   \n",
       "2                 NaN           N  Web-browser     B    S143     1         0   \n",
       "3                 NaN           N  Web-browser     B    S143     3         0   \n",
       "4                 NaN           N  Web-browser     B    S134     3         1   \n",
       "\n",
       "  Disbursed  \n",
       "0         0  \n",
       "1         0  \n",
       "2         0  \n",
       "3         0  \n",
       "4         0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 87019 entries, 0 to 87018\n",
      "Data columns (total 26 columns):\n",
      "ID                       87019 non-null object\n",
      "Gender                   87019 non-null object\n",
      "City                     86016 non-null object\n",
      "Monthly_Income           87019 non-null int64\n",
      "DOB                      87019 non-null object\n",
      "Lead_Creation_Date       87019 non-null object\n",
      "Loan_Amount_Applied      86948 non-null float64\n",
      "Loan_Tenure_Applied      86948 non-null float64\n",
      "Existing_EMI             86948 non-null float64\n",
      "Employer_Name            86948 non-null object\n",
      "Salary_Account           75255 non-null object\n",
      "Mobile_Verified          87019 non-null object\n",
      "Var5                     87019 non-null int64\n",
      "Var1                     87019 non-null object\n",
      "Loan_Amount_Submitted    52407 non-null float64\n",
      "Loan_Tenure_Submitted    52407 non-null float64\n",
      "Interest_Rate            27726 non-null float64\n",
      "Processing_Fee           27420 non-null float64\n",
      "EMI_Loan_Submitted       27726 non-null float64\n",
      "Filled_Form              87019 non-null object\n",
      "Device_Type              87019 non-null object\n",
      "Var2                     87019 non-null object\n",
      "Source                   87019 non-null object\n",
      "Var4                     87019 non-null int64\n",
      "LoggedIn                 87019 non-null int64\n",
      "Disbursed                87019 non-null int64\n",
      "dtypes: float64(8), int64(5), object(13)\n",
      "memory usage: 17.3+ MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "把缺失值全部用0填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>City</th>\n",
       "      <th>Monthly_Income</th>\n",
       "      <th>DOB</th>\n",
       "      <th>Lead_Creation_Date</th>\n",
       "      <th>Loan_Amount_Applied</th>\n",
       "      <th>Loan_Tenure_Applied</th>\n",
       "      <th>Existing_EMI</th>\n",
       "      <th>Employer_Name</th>\n",
       "      <th>...</th>\n",
       "      <th>Interest_Rate</th>\n",
       "      <th>Processing_Fee</th>\n",
       "      <th>EMI_Loan_Submitted</th>\n",
       "      <th>Filled_Form</th>\n",
       "      <th>Device_Type</th>\n",
       "      <th>Var2</th>\n",
       "      <th>Source</th>\n",
       "      <th>Var4</th>\n",
       "      <th>LoggedIn</th>\n",
       "      <th>Disbursed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID000002C20</td>\n",
       "      <td>Female</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>20000</td>\n",
       "      <td>23-May-78</td>\n",
       "      <td>15-May-15</td>\n",
       "      <td>300000</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>CYBOSOL</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>Web-browser</td>\n",
       "      <td>G</td>\n",
       "      <td>S122</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID000004E40</td>\n",
       "      <td>Male</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>35000</td>\n",
       "      <td>7-Oct-85</td>\n",
       "      <td>4-May-15</td>\n",
       "      <td>200000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>TATA CONSULTANCY SERVICES LTD (TCS)</td>\n",
       "      <td>...</td>\n",
       "      <td>13.25</td>\n",
       "      <td>0</td>\n",
       "      <td>6762.9</td>\n",
       "      <td>N</td>\n",
       "      <td>Web-browser</td>\n",
       "      <td>G</td>\n",
       "      <td>S122</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID000007H20</td>\n",
       "      <td>Male</td>\n",
       "      <td>Panchkula</td>\n",
       "      <td>22500</td>\n",
       "      <td>10-Oct-81</td>\n",
       "      <td>19-May-15</td>\n",
       "      <td>600000</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>ALCHEMIST HOSPITALS LTD</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>Web-browser</td>\n",
       "      <td>B</td>\n",
       "      <td>S143</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID000008I30</td>\n",
       "      <td>Male</td>\n",
       "      <td>Saharsa</td>\n",
       "      <td>35000</td>\n",
       "      <td>30-Nov-87</td>\n",
       "      <td>9-May-15</td>\n",
       "      <td>1e+06</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>BIHAR GOVERNMENT</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>Web-browser</td>\n",
       "      <td>B</td>\n",
       "      <td>S143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID000009J40</td>\n",
       "      <td>Male</td>\n",
       "      <td>Bengaluru</td>\n",
       "      <td>100000</td>\n",
       "      <td>17-Feb-84</td>\n",
       "      <td>20-May-15</td>\n",
       "      <td>500000</td>\n",
       "      <td>2</td>\n",
       "      <td>25000</td>\n",
       "      <td>GLOBAL EDGE SOFTWARE</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>Web-browser</td>\n",
       "      <td>B</td>\n",
       "      <td>S134</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID  Gender       City  Monthly_Income        DOB  \\\n",
       "0  ID000002C20  Female      Delhi           20000  23-May-78   \n",
       "1  ID000004E40    Male     Mumbai           35000   7-Oct-85   \n",
       "2  ID000007H20    Male  Panchkula           22500  10-Oct-81   \n",
       "3  ID000008I30    Male    Saharsa           35000  30-Nov-87   \n",
       "4  ID000009J40    Male  Bengaluru          100000  17-Feb-84   \n",
       "\n",
       "  Lead_Creation_Date Loan_Amount_Applied Loan_Tenure_Applied Existing_EMI  \\\n",
       "0          15-May-15              300000                   5            0   \n",
       "1           4-May-15              200000                   2            0   \n",
       "2          19-May-15              600000                   4            0   \n",
       "3           9-May-15               1e+06                   5            0   \n",
       "4          20-May-15              500000                   2        25000   \n",
       "\n",
       "                         Employer_Name    ...    Interest_Rate Processing_Fee  \\\n",
       "0                              CYBOSOL    ...                0              0   \n",
       "1  TATA CONSULTANCY SERVICES LTD (TCS)    ...            13.25              0   \n",
       "2              ALCHEMIST HOSPITALS LTD    ...                0              0   \n",
       "3                     BIHAR GOVERNMENT    ...                0              0   \n",
       "4                 GLOBAL EDGE SOFTWARE    ...                0              0   \n",
       "\n",
       "   EMI_Loan_Submitted Filled_Form  Device_Type Var2 Source Var4 LoggedIn  \\\n",
       "0                   0           N  Web-browser    G   S122    1        0   \n",
       "1              6762.9           N  Web-browser    G   S122    3        0   \n",
       "2                   0           N  Web-browser    B   S143    1        0   \n",
       "3                   0           N  Web-browser    B   S143    3        0   \n",
       "4                   0           N  Web-browser    B   S134    3        1   \n",
       "\n",
       "  Disbursed  \n",
       "0         0  \n",
       "1         0  \n",
       "2         0  \n",
       "3         0  \n",
       "4         0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train=train.fillna('0')\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 87019 entries, 0 to 87018\n",
      "Data columns (total 26 columns):\n",
      "ID                       87019 non-null object\n",
      "Gender                   87019 non-null object\n",
      "City                     87019 non-null object\n",
      "Monthly_Income           87019 non-null int64\n",
      "DOB                      87019 non-null object\n",
      "Lead_Creation_Date       87019 non-null object\n",
      "Loan_Amount_Applied      87019 non-null object\n",
      "Loan_Tenure_Applied      87019 non-null object\n",
      "Existing_EMI             87019 non-null object\n",
      "Employer_Name            87019 non-null object\n",
      "Salary_Account           87019 non-null object\n",
      "Mobile_Verified          87019 non-null object\n",
      "Var5                     87019 non-null int64\n",
      "Var1                     87019 non-null object\n",
      "Loan_Amount_Submitted    87019 non-null object\n",
      "Loan_Tenure_Submitted    87019 non-null object\n",
      "Interest_Rate            87019 non-null object\n",
      "Processing_Fee           87019 non-null object\n",
      "EMI_Loan_Submitted       87019 non-null object\n",
      "Filled_Form              87019 non-null object\n",
      "Device_Type              87019 non-null object\n",
      "Var2                     87019 non-null object\n",
      "Source                   87019 non-null object\n",
      "Var4                     87019 non-null int64\n",
      "LoggedIn                 87019 non-null int64\n",
      "Disbursed                87019 non-null int64\n",
      "dtypes: int64(5), object(21)\n",
      "memory usage: 17.3+ MB\n"
     ]
    }
   ],
   "source": [
    "#再次查看，全部都有值了\n",
    "X_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gender不同取值和出现次数\n",
      "\n",
      "Male      49848\n",
      "Female    37171\n",
      "Name: Gender, dtype: int64\n",
      "\n",
      "\n",
      "City不同取值和出现次数\n",
      "\n",
      "Delhi                  12527\n",
      "Bengaluru              10824\n",
      "Mumbai                 10795\n",
      "Hyderabad               7272\n",
      "Chennai                 6916\n",
      "Pune                    5207\n",
      "Kolkata                 2888\n",
      "Ahmedabad               1788\n",
      "Jaipur                  1331\n",
      "Gurgaon                 1212\n",
      "Coimbatore              1147\n",
      "Thane                    905\n",
      "Chandigarh               870\n",
      "Surat                    802\n",
      "Visakhapatnam            764\n",
      "Indore                   733\n",
      "Vadodara                 624\n",
      "Nagpur                   594\n",
      "Lucknow                  580\n",
      "Ghaziabad                560\n",
      "Bhopal                   513\n",
      "Kochi                    492\n",
      "Patna                    461\n",
      "Faridabad                447\n",
      "Madurai                  375\n",
      "Noida                    373\n",
      "Gautam Buddha Nagar      338\n",
      "Dehradun                 314\n",
      "Raipur                   289\n",
      "Bhubaneswar              277\n",
      "                       ...  \n",
      "Shahpura                   1\n",
      "Mandla                     1\n",
      "Doda                       1\n",
      "KHAMBHAT                   1\n",
      "Sheopur                    1\n",
      "Dhalai                     1\n",
      "Seoni                      1\n",
      "Kannauj                    1\n",
      "LUNAWADA                   1\n",
      "Chandel                    1\n",
      "CHIKHLI (GUJ.)             1\n",
      "CHOTILA                    1\n",
      "Mainpuri                   1\n",
      "Nalbari                    1\n",
      "Bandipore                  1\n",
      "Narayanpur                 1\n",
      "Gadwal                     1\n",
      "Kargil                     1\n",
      "Sawai Madhopur             1\n",
      "Bageshwar                  1\n",
      "DHANDHUKA                  1\n",
      "Giridih                    1\n",
      "Lakhisarai                 1\n",
      "Madhepura                  1\n",
      "Poonch                     1\n",
      "Kabri Anglong              1\n",
      "Umaria                     1\n",
      "Kandhamal                  1\n",
      "Mokokchung                 1\n",
      "Dungarpur                  1\n",
      "Name: City, Length: 697, dtype: int64\n",
      "\n",
      "\n",
      "Mobile_Verified不同取值和出现次数\n",
      "\n",
      "Y    56481\n",
      "N    30538\n",
      "Name: Mobile_Verified, dtype: int64\n",
      "\n",
      "\n",
      "Var1不同取值和出现次数\n",
      "\n",
      "HBXX    59293\n",
      "HBXC     9010\n",
      "HBXB     4479\n",
      "HAXA     2909\n",
      "HBXA     2123\n",
      "HAXB     2011\n",
      "HBXD     1964\n",
      "HAXC     1536\n",
      "HBXH      970\n",
      "HCXF      722\n",
      "HAYT      508\n",
      "HAVC      384\n",
      "HAXM      268\n",
      "HCXD      237\n",
      "HCYS      217\n",
      "HVYS      186\n",
      "HAZD      109\n",
      "HCXG       78\n",
      "HAXF       15\n",
      "Name: Var1, dtype: int64\n",
      "\n",
      "\n",
      "Filled_Form不同取值和出现次数\n",
      "\n",
      "N    67529\n",
      "Y    19490\n",
      "Name: Filled_Form, dtype: int64\n",
      "\n",
      "\n",
      "Source不同取值和出现次数\n",
      "\n",
      "S122    38566\n",
      "S133    29885\n",
      "S159     5599\n",
      "S143     4332\n",
      "S127     1931\n",
      "S137     1724\n",
      "S134     1301\n",
      "S161      769\n",
      "S151      720\n",
      "S157      650\n",
      "S153      494\n",
      "S156      308\n",
      "S144      299\n",
      "S158      208\n",
      "S123       73\n",
      "S141       57\n",
      "S162       36\n",
      "S124       24\n",
      "S160       11\n",
      "S150       10\n",
      "S155        4\n",
      "S129        3\n",
      "S136        3\n",
      "S139        3\n",
      "S138        3\n",
      "S135        2\n",
      "S125        1\n",
      "S140        1\n",
      "S130        1\n",
      "S154        1\n",
      "Name: Source, dtype: int64\n",
      "\n",
      "\n",
      "Var2不同取值和出现次数\n",
      "\n",
      "B    37280\n",
      "G    33031\n",
      "C    14210\n",
      "E     1315\n",
      "D      634\n",
      "F      544\n",
      "A        5\n",
      "Name: Var2, dtype: int64\n",
      "\n",
      "\n",
      "Var4不同取值和出现次数\n",
      "\n",
      "3    25260\n",
      "1    23905\n",
      "5    20266\n",
      "4     6577\n",
      "2     5931\n",
      "0     2546\n",
      "7     2302\n",
      "6      232\n",
      "Name: Var4, dtype: int64\n",
      "\n",
      "\n",
      "Device_Type不同取值和出现次数\n",
      "\n",
      "Web-browser    64316\n",
      "Mobile         22703\n",
      "Name: Device_Type, dtype: int64\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#找到其中的类别型特征，进行分析和处理\n",
    "categorical_geatres=['Gender','City','Mobile_Verified','Var1','Filled_Form','Source','Var2','Var4','Device_Type']\n",
    "for col in categorical_geatres:\n",
    "    print('%s不同取值和出现次数\\n'%col)\n",
    "    print(train[col].value_counts())\n",
    "    print('\\n')\n",
    "#从上面得出的结论有： City数量太多，要思考如何处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PS.有些类别函数会单独出现1个，说明存在错误的数据，需要剔除经过查看数据，发现是一条错误数据导致的，删除后就好了\n",
    "此处我通过trian=train.drop(62691),即直接找到出问题的行，然后删除行的方式来处理，执行是成功的，查看info 的时候也少了一行，但是用 print(train[col].value_counts())方式查看的时候，这个类别还是没有消失，所以我想问问原因，以及如何正确的处理发现的坏数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上述的类别可以分析：\n",
    "1. Gender只有2个，可以直接用labelencoder编码\n",
    "2. City类别太多有600多个，可以使用区间计算\n",
    "3. Mobile_Verified只有2类，同样使用labelencoder编码\n",
    "4. Var1类别也不多，用labelencoder编码或者独热编码\n",
    "5. Filled_Form只有2类，同样使用labelencoder编码\n",
    "6. Source类别有30多个，同样使用labelencoder编码\n",
    "7. Var2类别少，同样使用labelencoder编码\n",
    "8. var4本身是数字类的，不处理即可\n",
    "9. Device_Type只有2类，同样使用labelencoder编码"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将离散型特征进行one-hot编码的作用，是为了让距离计算更合理，但如果特征是离散的，并且不用one-hot编码就可以很合理的计算出距离，那么就没必要进行one-hot编码。 \n",
    "有些基于树的算法在处理变量时，并不是基于向量空间度量，数值只是个类别符号，即没有偏序关系，所以不用进行独热编码。  \n",
    "Tree Model不太需要one-hot编码： 对于决策树来说，one-hot的本质是增加树的深度\n",
    "考虑到我们后面要用的是XGBoost,所以我们这边就不再用独热编码了"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用区间计数处理City"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Delhi                  12527\n",
       "Bengaluru              10824\n",
       "Mumbai                 10795\n",
       "Hyderabad               7272\n",
       "Chennai                 6916\n",
       "Pune                    5207\n",
       "Kolkata                 2888\n",
       "Ahmedabad               1788\n",
       "Jaipur                  1331\n",
       "Gurgaon                 1212\n",
       "Coimbatore              1147\n",
       "Thane                    905\n",
       "Chandigarh               870\n",
       "Surat                    802\n",
       "Visakhapatnam            764\n",
       "Indore                   733\n",
       "Vadodara                 624\n",
       "Nagpur                   594\n",
       "Lucknow                  580\n",
       "Ghaziabad                560\n",
       "Bhopal                   513\n",
       "Kochi                    492\n",
       "Patna                    461\n",
       "Faridabad                447\n",
       "Madurai                  375\n",
       "Noida                    373\n",
       "Gautam Buddha Nagar      338\n",
       "Dehradun                 314\n",
       "Raipur                   289\n",
       "Bhubaneswar              277\n",
       "                       ...  \n",
       "Shahpura                   1\n",
       "Mandla                     1\n",
       "Doda                       1\n",
       "KHAMBHAT                   1\n",
       "Sheopur                    1\n",
       "Dhalai                     1\n",
       "Seoni                      1\n",
       "Kannauj                    1\n",
       "LUNAWADA                   1\n",
       "Chandel                    1\n",
       "CHIKHLI (GUJ.)             1\n",
       "CHOTILA                    1\n",
       "Mainpuri                   1\n",
       "Nalbari                    1\n",
       "Bandipore                  1\n",
       "Narayanpur                 1\n",
       "Gadwal                     1\n",
       "Kargil                     1\n",
       "Sawai Madhopur             1\n",
       "Bageshwar                  1\n",
       "DHANDHUKA                  1\n",
       "Giridih                    1\n",
       "Lakhisarai                 1\n",
       "Madhepura                  1\n",
       "Poonch                     1\n",
       "Kabri Anglong              1\n",
       "Umaria                     1\n",
       "Kandhamal                  1\n",
       "Mokokchung                 1\n",
       "Dungarpur                  1\n",
       "Name: City, Length: 697, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['City'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_city=train['City']\n",
    "city_counts=train['City'].value_counts()\n",
    "city_index=city_counts.index\n",
    "city_value=city_counts.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pd.to_numeric(X_train_city)\n",
    "#X_train_city=X_train_city.astype('str')\n",
    "X_train_city=X_train_city.to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "城市类型太多，根据出现次数分区，之后再用标签编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in city_index:\n",
    "    #print(i,city_counts[i])\n",
    "    if(city_counts[i]>10000):\n",
    "        X_train_city=X_train_city.replace(i,'Large City')\n",
    "    elif(5000<city_counts[i]<10000):\n",
    "        X_train_city=X_train_city.replace(i,'Big City')\n",
    "    elif(1000<city_counts[i]<5000):\n",
    "        X_train_city=X_train_city.replace(i,'Medine City')\n",
    "    elif(300<city_counts[i]<1000):\n",
    "        X_train_city=X_train_city.replace(i,'Small City')\n",
    "    else:\n",
    "        X_train_city=X_train_city.replace(i,'Mini City')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Large City</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Large City</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mini City</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mini City</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Large City</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Large City</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Mini City</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Large City</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Small City</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Large City</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         City\n",
       "0  Large City\n",
       "1  Large City\n",
       "2   Mini City\n",
       "3   Mini City\n",
       "4  Large City\n",
       "5  Large City\n",
       "6   Mini City\n",
       "7  Large City\n",
       "8  Small City\n",
       "9  Large City"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_city.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "处理出生年月Dob\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_dob=train['DOB']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    23-May-78\n",
       "1     7-Oct-85\n",
       "2    10-Oct-81\n",
       "3    30-Nov-87\n",
       "4    17-Feb-84\n",
       "5    21-Apr-82\n",
       "6    23-Oct-87\n",
       "7    25-Jul-75\n",
       "8    26-Jan-72\n",
       "9    12-Sep-89\n",
       "Name: DOB, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_dob.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到，这边的出生日期里面的月和日其实对最后的结果影响应该不大，所以将出生日期提取成年龄会比较好一些，处理方案是保留最后2位即可，如果换算成年龄会直观一些，但是被减数无法确定，不如直接用出生的年份即可"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_dob=X_train_dob.str.split('-',expand=True)[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "获取后待会直接转成int标准化一下就行了"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "处理Lead_Creation_Date字段"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    15-May-15\n",
       "1     4-May-15\n",
       "2    19-May-15\n",
       "3     9-May-15\n",
       "4    20-May-15\n",
       "5    20-May-15\n",
       "6     1-May-15\n",
       "7    20-May-15\n",
       "8     2-May-15\n",
       "9     3-May-15\n",
       "Name: Lead_Creation_Date, dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_LCD=train['Lead_Creation_Date']\n",
    "X_train_LCD.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这个值的含义是“潜在（贷款）创建日期”，业务上简单一点， 可以取中间的月份即可。因为前后的两个值几乎是一样的，所以此次直接取中间月份，然后用标签编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_LCD=X_train_LCD.str.split('-',expand=True)[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    May\n",
       "1    May\n",
       "2    May\n",
       "3    May\n",
       "4    May\n",
       "5    May\n",
       "6    May\n",
       "7    May\n",
       "8    May\n",
       "9    May\n",
       "Name: 1, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_LCD.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "处理Employer_Name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_EN=train['Employer_Name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CYBOSOL', 'TATA CONSULTANCY SERVICES LTD (TCS)',\n",
       "       'ALCHEMIST HOSPITALS LTD', ..., 'UTTAM VALUE STEEL LTD,WARDHA',\n",
       "       'MAYO COLLEGE', 'BANGALORE INSTITUTE OF TECHNOLOGY'], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_EN.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0                                                  4914\n",
      "TATA CONSULTANCY SERVICES LTD (TCS)                 550\n",
      "COGNIZANT TECHNOLOGY SOLUTIONS INDIA PVT LTD        404\n",
      "ACCENTURE SERVICES PVT LTD                          324\n",
      "GOOGLE                                              301\n",
      "HCL TECHNOLOGIES LTD                                250\n",
      "ICICI BANK LTD                                      239\n",
      "INDIAN AIR FORCE                                    191\n",
      "INFOSYS TECHNOLOGIES                                181\n",
      "GENPACT                                             179\n",
      "IBM CORPORATION                                     173\n",
      "INDIAN ARMY                                         171\n",
      "TYPE SLOWLY FOR AUTO FILL                           162\n",
      "WIPRO TECHNOLOGIES                                  155\n",
      "HDFC BANK LTD                                       148\n",
      "IKYA HUMAN CAPITAL SOLUTIONS LTD                    142\n",
      "STATE GOVERNMENT                                    134\n",
      "INDIAN RAILWAY                                      130\n",
      "INDIAN NAVY                                         128\n",
      "ARMY                                                126\n",
      "WIPRO BPO                                           116\n",
      "OTHERS                                              115\n",
      "TECH MAHINDRA LTD                                   113\n",
      "CONVERGYS INDIA SERVICES PVT LTD                    113\n",
      "SERCO BPO PVT LTD                                   108\n",
      "IBM GLOBAL SERVICES INDIA LTD                       104\n",
      "CONCENTRIX DAKSH SERVICES INDIA PVT LTD              99\n",
      "RANDSTAD INDIA LTD                                   96\n",
      "CAPGEMINI INDIA PVT LTD                              96\n",
      "ADECCO INDIA PVT LTD                                 95\n",
      "                                                   ... \n",
      "TREND SETTERS INTERNATIONAL                           1\n",
      "LAKSHYA PHARMA PVT LTD                                1\n",
      "B SAANTOSHKUMAR                                       1\n",
      "NPR COLLEGE OF ENGINEERING & TECHNOLOGY, NATHAM       1\n",
      "MAGPPIE INTERNATIONAL LTD                             1\n",
      "PRUMATECH INFOSYSTEMS PVT LTD                         1\n",
      "SHIVANANDA B                                          1\n",
      "SISIKIN INSTRUMENTS COMPANY PVT LTD                   1\n",
      "GOVT ITI                                              1\n",
      "RRIMT LKO                                             1\n",
      "SOCGEN                                                1\n",
      "PRITHVIRAJ                                            1\n",
      "LIGHTHOUSE INFOSYSTEMS PVT LTD                        1\n",
      "SDB                                                   1\n",
      "KEVIN ENTERPRISER PVT LTD                             1\n",
      "PRANAM DECOR                                          1\n",
      "PORUS SOFTWARE                                        1\n",
      "SWAMY SONS AGENCIES PVT LTD                           1\n",
      "JR TECHNOLOGIES                                       1\n",
      "ANTI CORRUPTION BUREAU                                1\n",
      "GUPTAFABTEX PVT.LMT                                   1\n",
      "SYSTECH INFRACORE PVT LTD                             1\n",
      "PUYVAST MARITIME INDIA LTD                            1\n",
      "VIEW CRAFT INDIA PVT LTD                              1\n",
      "SRI VELMURUGAN AND COMPANY                            1\n",
      "ROSSEL TECHSYS - DIVISION OF ROSSEL INDIA LTD         1\n",
      "THIRDWARE TECHNOLOGY SOLUTIONS                        1\n",
      "JAIPURIA INSTITUTE OF MANAGEMENT                      1\n",
      "SHRWAN RAM BHAKAR                                     1\n",
      "APEX ACADEMY                                          1\n",
      "Name: Employer_Name, Length: 43566, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(train['Employer_Name'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "查看数据得一共8w多条数据，这个类别就有4w多个，实际场景中这个是借贷人，因为太多，目前不知道怎么处理，后面有个是借贷金额，所以其实有其他的借贷金额在，这个借贷人的信息就不是那么重要。\n",
    "而且之前查看是否转化成缺省值的时候发现增加一个是否缺省或者将这一类有值变为1，没值变成0.这两种方案对结果影响不大。\n",
    "为了方便计算，直接删除这个字段信息"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "处理Salary_Account"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_SA=train['Salary_Account']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HDFC Bank                                          17695\n",
      "ICICI Bank                                         13636\n",
      "State Bank of India                                11843\n",
      "Axis Bank                                           8783\n",
      "Citibank                                            2376\n",
      "Kotak Bank                                          2067\n",
      "IDBI Bank                                           1550\n",
      "Punjab National Bank                                1201\n",
      "Bank of India                                       1170\n",
      "Bank of Baroda                                      1126\n",
      "Standard Chartered Bank                              995\n",
      "Canara Bank                                          989\n",
      "Union Bank of India                                  951\n",
      "Yes Bank                                             779\n",
      "ING Vysya                                            678\n",
      "Corporation bank                                     649\n",
      "Indian Overseas Bank                                 612\n",
      "State Bank of Hyderabad                              597\n",
      "Indian Bank                                          555\n",
      "Oriental Bank of Commerce                            524\n",
      "IndusInd Bank                                        503\n",
      "Andhra Bank                                          485\n",
      "Central Bank of India                                445\n",
      "Syndicate Bank                                       415\n",
      "Bank of Maharasthra                                  406\n",
      "State Bank of Bikaner & Jaipur                       331\n",
      "HSBC                                                 328\n",
      "Karur Vysya Bank                                     326\n",
      "State Bank of Mysore                                 255\n",
      "Federal Bank                                         253\n",
      "Vijaya Bank                                          252\n",
      "Allahabad Bank                                       238\n",
      "UCO Bank                                             237\n",
      "State Bank of Travancore                             227\n",
      "Karnataka Bank                                       200\n",
      "Saraswat Bank                                        195\n",
      "United Bank of India                                 183\n",
      "Dena Bank                                            182\n",
      "State Bank of Patiala                                177\n",
      "South Indian Bank                                    160\n",
      "Deutsche Bank                                        125\n",
      "Abhyuday Co-op Bank Ltd                              108\n",
      "The Ratnakar Bank Ltd                                 83\n",
      "Tamil Nadu Mercantile Bank                            71\n",
      "Punjab & Sind bank                                    66\n",
      "J&K Bank                                              59\n",
      "Lakshmi Vilas bank                                    50\n",
      "Dhanalakshmi Bank Ltd                                 42\n",
      "State Bank of Indore                                  18\n",
      "Catholic Syrian Bank                                  14\n",
      "India Bulls                                           11\n",
      "B N P Paribas                                          8\n",
      "GIC Housing Finance Ltd                                8\n",
      "Firstrand Bank Limited                                 7\n",
      "Bank of Rajasthan                                      5\n",
      "Kerala Gramin Bank                                     4\n",
      "Industrial And Commercial Bank Of China Limited        2\n",
      "Name: Salary_Account, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(X_train_SA.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里类别不是很多，然后实际场景中银行其实还是蛮重要的，这里直接用标签编码，其实想用哈希编码或者二进制编码，但是不会。。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将所有处理的数据开始拼接"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 标签\n",
    "y_train = train['Disbursed'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 87019 entries, 0 to 87018\n",
      "Data columns (total 26 columns):\n",
      "ID                       87019 non-null object\n",
      "Gender                   87019 non-null object\n",
      "City                     87019 non-null object\n",
      "Monthly_Income           87019 non-null int64\n",
      "DOB                      87019 non-null object\n",
      "Lead_Creation_Date       87019 non-null object\n",
      "Loan_Amount_Applied      87019 non-null object\n",
      "Loan_Tenure_Applied      87019 non-null object\n",
      "Existing_EMI             87019 non-null object\n",
      "Employer_Name            87019 non-null object\n",
      "Salary_Account           87019 non-null object\n",
      "Mobile_Verified          87019 non-null object\n",
      "Var5                     87019 non-null int64\n",
      "Var1                     87019 non-null object\n",
      "Loan_Amount_Submitted    87019 non-null object\n",
      "Loan_Tenure_Submitted    87019 non-null object\n",
      "Interest_Rate            87019 non-null object\n",
      "Processing_Fee           87019 non-null object\n",
      "EMI_Loan_Submitted       87019 non-null object\n",
      "Filled_Form              87019 non-null object\n",
      "Device_Type              87019 non-null object\n",
      "Var2                     87019 non-null object\n",
      "Source                   87019 non-null object\n",
      "Var4                     87019 non-null int64\n",
      "LoggedIn                 87019 non-null int64\n",
      "Disbursed                87019 non-null int64\n",
      "dtypes: int64(5), object(21)\n",
      "memory usage: 17.3+ MB\n"
     ]
    }
   ],
   "source": [
    "X_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "needlabel=['Gender','Mobile_Verified','Var1','Filled_Form','Source','Var2','Device_Type']\n",
    "X_train_label=X_train[needlabel]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "\n",
    "def labelToInt(label):\n",
    "    train_label=X_train[label].value_counts().index\n",
    "    le.fit(train_label)\n",
    "    X_train[label]=le.transform(X_train[label])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "labelToInt('Gender')\n",
    "labelToInt('Mobile_Verified')\n",
    "labelToInt('Var1')\n",
    "labelToInt('Filled_Form')\n",
    "labelToInt('Source')\n",
    "labelToInt('Var2')\n",
    "labelToInt('Device_Type')\n",
    "labelToInt('Salary_Account')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>City</th>\n",
       "      <th>Monthly_Income</th>\n",
       "      <th>DOB</th>\n",
       "      <th>Lead_Creation_Date</th>\n",
       "      <th>Loan_Amount_Applied</th>\n",
       "      <th>Loan_Tenure_Applied</th>\n",
       "      <th>Existing_EMI</th>\n",
       "      <th>Employer_Name</th>\n",
       "      <th>...</th>\n",
       "      <th>Interest_Rate</th>\n",
       "      <th>Processing_Fee</th>\n",
       "      <th>EMI_Loan_Submitted</th>\n",
       "      <th>Filled_Form</th>\n",
       "      <th>Device_Type</th>\n",
       "      <th>Var2</th>\n",
       "      <th>Source</th>\n",
       "      <th>Var4</th>\n",
       "      <th>LoggedIn</th>\n",
       "      <th>Disbursed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID000002C20</td>\n",
       "      <td>0</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>20000</td>\n",
       "      <td>23-May-78</td>\n",
       "      <td>15-May-15</td>\n",
       "      <td>300000</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>CYBOSOL</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID000004E40</td>\n",
       "      <td>1</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>35000</td>\n",
       "      <td>7-Oct-85</td>\n",
       "      <td>4-May-15</td>\n",
       "      <td>200000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>TATA CONSULTANCY SERVICES LTD (TCS)</td>\n",
       "      <td>...</td>\n",
       "      <td>13.25</td>\n",
       "      <td>0</td>\n",
       "      <td>6762.9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID000007H20</td>\n",
       "      <td>1</td>\n",
       "      <td>Panchkula</td>\n",
       "      <td>22500</td>\n",
       "      <td>10-Oct-81</td>\n",
       "      <td>19-May-15</td>\n",
       "      <td>600000</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>ALCHEMIST HOSPITALS LTD</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID000008I30</td>\n",
       "      <td>1</td>\n",
       "      <td>Saharsa</td>\n",
       "      <td>35000</td>\n",
       "      <td>30-Nov-87</td>\n",
       "      <td>9-May-15</td>\n",
       "      <td>1e+06</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>BIHAR GOVERNMENT</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID000009J40</td>\n",
       "      <td>1</td>\n",
       "      <td>Bengaluru</td>\n",
       "      <td>100000</td>\n",
       "      <td>17-Feb-84</td>\n",
       "      <td>20-May-15</td>\n",
       "      <td>500000</td>\n",
       "      <td>2</td>\n",
       "      <td>25000</td>\n",
       "      <td>GLOBAL EDGE SOFTWARE</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID  Gender       City  Monthly_Income        DOB  \\\n",
       "0  ID000002C20       0      Delhi           20000  23-May-78   \n",
       "1  ID000004E40       1     Mumbai           35000   7-Oct-85   \n",
       "2  ID000007H20       1  Panchkula           22500  10-Oct-81   \n",
       "3  ID000008I30       1    Saharsa           35000  30-Nov-87   \n",
       "4  ID000009J40       1  Bengaluru          100000  17-Feb-84   \n",
       "\n",
       "  Lead_Creation_Date Loan_Amount_Applied Loan_Tenure_Applied Existing_EMI  \\\n",
       "0          15-May-15              300000                   5            0   \n",
       "1           4-May-15              200000                   2            0   \n",
       "2          19-May-15              600000                   4            0   \n",
       "3           9-May-15               1e+06                   5            0   \n",
       "4          20-May-15              500000                   2        25000   \n",
       "\n",
       "                         Employer_Name    ...      Interest_Rate  \\\n",
       "0                              CYBOSOL    ...                  0   \n",
       "1  TATA CONSULTANCY SERVICES LTD (TCS)    ...              13.25   \n",
       "2              ALCHEMIST HOSPITALS LTD    ...                  0   \n",
       "3                     BIHAR GOVERNMENT    ...                  0   \n",
       "4                 GLOBAL EDGE SOFTWARE    ...                  0   \n",
       "\n",
       "   Processing_Fee  EMI_Loan_Submitted  Filled_Form Device_Type Var2 Source  \\\n",
       "0               0                   0            0           1    6      0   \n",
       "1               0              6762.9            0           1    6      0   \n",
       "2               0                   0            0           1    1     16   \n",
       "3               0                   0            0           1    1     16   \n",
       "4               0                   0            0           1    1      8   \n",
       "\n",
       "  Var4 LoggedIn  Disbursed  \n",
       "0    1        0          0  \n",
       "1    3        0          0  \n",
       "2    1        0          0  \n",
       "3    3        0          0  \n",
       "4    3        1          0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对处理好的City，Lead_Creation_Date进行标签编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Big City', 'Large City', 'Medine City', 'Mini City', 'Small City']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_label=X_train_city['City'].value_counts().index\n",
    "le.fit(train_label)\n",
    "list(le.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_city=X_train_city['City']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_city_label=le.fit_transform(train_city.astype(str))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 3, ..., 1, 1, 1])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_city_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train['City']=train_city_label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "处理Lead_Creation_Date：X_train_LCD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Jul', 'Jun', 'May']"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_label=X_train_LCD.value_counts().index\n",
    "le.fit(train_label)\n",
    "list(le.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    " X_train['Lead_Creation_Date']=le.transform(X_train_LCD)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "处理DOB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train['DOB']=X_train_dob.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>City</th>\n",
       "      <th>Monthly_Income</th>\n",
       "      <th>DOB</th>\n",
       "      <th>Lead_Creation_Date</th>\n",
       "      <th>Loan_Amount_Applied</th>\n",
       "      <th>Loan_Tenure_Applied</th>\n",
       "      <th>Existing_EMI</th>\n",
       "      <th>Employer_Name</th>\n",
       "      <th>...</th>\n",
       "      <th>Interest_Rate</th>\n",
       "      <th>Processing_Fee</th>\n",
       "      <th>EMI_Loan_Submitted</th>\n",
       "      <th>Filled_Form</th>\n",
       "      <th>Device_Type</th>\n",
       "      <th>Var2</th>\n",
       "      <th>Source</th>\n",
       "      <th>Var4</th>\n",
       "      <th>LoggedIn</th>\n",
       "      <th>Disbursed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>CYBOSOL</td>\n",
       "      <td>...</td>\n",
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       "      <td>2</td>\n",
       "      <td>200000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>TATA CONSULTANCY SERVICES LTD (TCS)</td>\n",
       "      <td>...</td>\n",
       "      <td>13.25</td>\n",
       "      <td>0</td>\n",
       "      <td>6762.9</td>\n",
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       "      <td>2</td>\n",
       "      <td>600000</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>ALCHEMIST HOSPITALS LTD</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>BIHAR GOVERNMENT</td>\n",
       "      <td>...</td>\n",
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       "      <td>500000</td>\n",
       "      <td>2</td>\n",
       "      <td>25000</td>\n",
       "      <td>GLOBAL EDGE SOFTWARE</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "            ID  Gender  City  Monthly_Income  DOB  Lead_Creation_Date  \\\n",
       "0  ID000002C20       0     1           20000   78                   2   \n",
       "1  ID000004E40       1     1           35000   85                   2   \n",
       "2  ID000007H20       1     3           22500   81                   2   \n",
       "3  ID000008I30       1     3           35000   87                   2   \n",
       "4  ID000009J40       1     1          100000   84                   2   \n",
       "\n",
       "  Loan_Amount_Applied Loan_Tenure_Applied Existing_EMI  \\\n",
       "0              300000                   5            0   \n",
       "1              200000                   2            0   \n",
       "2              600000                   4            0   \n",
       "3               1e+06                   5            0   \n",
       "4              500000                   2        25000   \n",
       "\n",
       "                         Employer_Name    ...      Interest_Rate  \\\n",
       "0                              CYBOSOL    ...                  0   \n",
       "1  TATA CONSULTANCY SERVICES LTD (TCS)    ...              13.25   \n",
       "2              ALCHEMIST HOSPITALS LTD    ...                  0   \n",
       "3                     BIHAR GOVERNMENT    ...                  0   \n",
       "4                 GLOBAL EDGE SOFTWARE    ...                  0   \n",
       "\n",
       "   Processing_Fee  EMI_Loan_Submitted  Filled_Form Device_Type Var2 Source  \\\n",
       "0               0                   0            0           1    6      0   \n",
       "1               0              6762.9            0           1    6      0   \n",
       "2               0                   0            0           1    1     16   \n",
       "3               0                   0            0           1    1     16   \n",
       "4               0                   0            0           1    1      8   \n",
       "\n",
       "  Var4 LoggedIn  Disbursed  \n",
       "0    1        0          0  \n",
       "1    3        0          0  \n",
       "2    1        0          0  \n",
       "3    3        0          0  \n",
       "4    3        1          0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train= X_train.drop([\"ID\",\"Employer_Name\",\"LoggedIn\",\"Disbursed\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 87019 entries, 0 to 87018\n",
      "Data columns (total 22 columns):\n",
      "Gender                   87019 non-null int32\n",
      "City                     87019 non-null int32\n",
      "Monthly_Income           87019 non-null int64\n",
      "DOB                      87019 non-null int32\n",
      "Lead_Creation_Date       87019 non-null int32\n",
      "Loan_Amount_Applied      87019 non-null object\n",
      "Loan_Tenure_Applied      87019 non-null object\n",
      "Existing_EMI             87019 non-null object\n",
      "Salary_Account           87019 non-null int32\n",
      "Mobile_Verified          87019 non-null int32\n",
      "Var5                     87019 non-null int64\n",
      "Var1                     87019 non-null int32\n",
      "Loan_Amount_Submitted    87019 non-null object\n",
      "Loan_Tenure_Submitted    87019 non-null object\n",
      "Interest_Rate            87019 non-null object\n",
      "Processing_Fee           87019 non-null object\n",
      "EMI_Loan_Submitted       87019 non-null object\n",
      "Filled_Form              87019 non-null int32\n",
      "Device_Type              87019 non-null int32\n",
      "Var2                     87019 non-null int32\n",
      "Source                   87019 non-null int32\n",
      "Var4                     87019 non-null int64\n",
      "dtypes: int32(11), int64(3), object(8)\n",
      "memory usage: 11.0+ MB\n"
     ]
    }
   ],
   "source": [
    "X_train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对数据进行缩放"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 对原始数据缩放\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "y_train = train['Disbursed']\n",
    "y = pd.Series(data = y_train, name = 'Disbursed')\n",
    "\n",
    "# 构造输入特征的标准化器\n",
    "#ms_org = MinMaxScaler()\n",
    "\n",
    "#保存特征名字，用于结果保存为csv\n",
    "feat_names_org = X_train.columns\n",
    "\n",
    "# 用训练训练模型（得到均值和标准差）：fit\n",
    "# 并对训练数据进行特征缩放：transform\n",
    "#X_train_org = ms_org.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_org = pd.concat([pd.DataFrame(columns = feat_names_org, data = X_train), y], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>18.25</td>\n",
       "      <td>1500</td>\n",
       "      <td>10883.4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  City  Monthly_Income  DOB  Lead_Creation_Date Loan_Amount_Applied  \\\n",
       "0       0     1           20000   78                   2              300000   \n",
       "1       1     1           35000   85                   2              200000   \n",
       "2       1     3           22500   81                   2              600000   \n",
       "3       1     3           35000   87                   2               1e+06   \n",
       "4       1     1          100000   84                   2              500000   \n",
       "5       1     1           45000   82                   2              300000   \n",
       "6       0     3           70000   87                   2                   6   \n",
       "7       1     1           20000   75                   2              200000   \n",
       "8       1     4           75000   72                   2                   0   \n",
       "9       0     1           30000   89                   2              300000   \n",
       "\n",
       "  Loan_Tenure_Applied Existing_EMI  Salary_Account  Mobile_Verified  \\\n",
       "0                   5            0              21                0   \n",
       "1                   2            0              23                1   \n",
       "2                   4            0              45                1   \n",
       "3                   5            0              45                1   \n",
       "4                   2        25000              21                1   \n",
       "5                   5        15000              22                1   \n",
       "6                   5            0              57                0   \n",
       "7                   5         2597               0                1   \n",
       "8                   0            0              45                1   \n",
       "9                   3            0              35                1   \n",
       "\n",
       "     ...      Loan_Tenure_Submitted  Interest_Rate Processing_Fee  \\\n",
       "0    ...                          0              0              0   \n",
       "1    ...                          2          13.25              0   \n",
       "2    ...                          4              0              0   \n",
       "3    ...                          5              0              0   \n",
       "4    ...                          2              0              0   \n",
       "5    ...                          5          13.99           1500   \n",
       "6    ...                          0              0              0   \n",
       "7    ...                          5              0              0   \n",
       "8    ...                          5          14.85          26000   \n",
       "9    ...                          3          18.25           1500   \n",
       "\n",
       "  EMI_Loan_Submitted Filled_Form Device_Type Var2  Source  Var4  Disbursed  \n",
       "0                  0           0           1    6       0     1          0  \n",
       "1             6762.9           0           1    6       0     3          0  \n",
       "2                  0           0           1    1      16     1          0  \n",
       "3                  0           0           1    1      16     3          0  \n",
       "4                  0           0           1    1       8     3          0  \n",
       "5            6978.92           0           1    1      16     3          0  \n",
       "6                  0           0           1    1       7     1          0  \n",
       "7                  0           0           1    1      26     3          0  \n",
       "8            30824.7           1           0    2       0     5          0  \n",
       "9            10883.4           0           1    1       7     1          0  \n",
       "\n",
       "[10 rows x 23 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_org.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_org.to_csv('HappyBank_train_org.csv',index=False,header=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 综上，此次特征工程基本上就是标签编码，因为后面用的是树模型，所以数据量纲化都不用了。以下几个字段的处理比较特殊：\n",
    "1. City：根据出现的次数分成超大，大，中等，小，迷你城市，然后用标签编码\n",
    "2. DOB：出生年月日，只保留了出生的年份，然后标签编码\n",
    "3. Lead_Creation_Date：贷款日期，保留了月份，然后标签编码\n",
    "4. Employer_Name：借贷人，种类太多，直接删除"
   ]
  }
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